garage.tf.models.gaussian_mlp_model¶
Gaussian MLP Model.
A model represented by a Gaussian distribution which is parameterized by a multilayer perceptron (MLP).
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class
GaussianMLPModel(output_dim, name=None, hidden_sizes=(32, 32), hidden_nonlinearity=tf.nn.tanh, hidden_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), hidden_b_init=tf.zeros_initializer(), output_nonlinearity=None, output_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), output_b_init=tf.zeros_initializer(), learn_std=True, adaptive_std=False, std_share_network=False, init_std=1.0, min_std=1e-06, max_std=None, std_hidden_sizes=(32, 32), std_hidden_nonlinearity=tf.nn.tanh, std_hidden_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), std_hidden_b_init=tf.zeros_initializer(), std_output_nonlinearity=None, std_output_w_init=tf.initializers.glorot_uniform(seed=deterministic.get_tf_seed_stream()), std_parameterization='exp', layer_normalization=False)¶ Bases:
garage.tf.models.model.Model
Gaussian MLP Model.
A model represented by a Gaussian distribution which is parameterized by a multilayer perceptron (MLP).
Parameters: - output_dim (int) – Output dimension of the model.
- name (str) – Model name, also the variable scope.
- hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for mean. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
- hidden_nonlinearity (callable) – Activation function for intermediate dense layer(s). It should return a tf.Tensor. Set it to None to maintain a linear activation.
- hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s). The function should return a tf.Tensor.
- hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s). The function should return a tf.Tensor.
- output_nonlinearity (callable) – Activation function for output dense layer. It should return a tf.Tensor. Set it to None to maintain a linear activation.
- output_w_init (callable) – Initializer function for the weight of output dense layer(s). The function should return a tf.Tensor.
- output_b_init (callable) – Initializer function for the bias of output dense layer(s). The function should return a tf.Tensor.
- learn_std (bool) – Is std trainable.
- init_std (float) – Initial value for std.
- adaptive_std (bool) – Is std a neural network. If False, it will be a parameter.
- std_share_network (bool) – Boolean for whether mean and std share the same network.
- std_hidden_sizes (list[int]) – Output dimension of dense layer(s) for the MLP for std. For example, (32, 32) means the MLP consists of two hidden layers, each with 32 hidden units.
- min_std (float) – If not None, the std is at least the value of min_std, to avoid numerical issues.
- max_std (float) – If not None, the std is at most the value of max_std, to avoid numerical issues.
- std_hidden_nonlinearity (callable) – Nonlinearity for each hidden layer in the std network.
- std_hidden_w_init (callable) – Initializer function for the weight of intermediate dense layer(s) in the std network. The function should return a tf.Tensor.
- std_hidden_b_init (callable) – Initializer function for the bias of intermediate dense layer(s) in the std network. The function should return a tf.Tensor.
- std_output_nonlinearity (callable) – Activation function for output dense layer in the std network. It should return a tf.Tensor. Set it to None to maintain a linear activation.
- std_output_w_init (callable) – Initializer function for the weight of output dense layer(s) in the std network.
- std_parameterization (str) –
How the std should be parametrized. There are two options: - exp: the logarithm of the std will be stored, and applied a
exponential transformation- softplus: the std will be computed as log(1+exp(x))
- layer_normalization (bool) – Bool for using layer normalization or not.
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parameters¶ Parameters of the model.
Returns: Parameters Return type: np.ndarray
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name¶ Name (str) of the model.
This is also the variable scope of the model.
Returns: Name of the model. Return type: str
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input¶ Default input of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the input of the network.
Returns: Default input of the model. Return type: tf.Tensor
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output¶ Default output of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the output of the network.
Returns: Default output of the model. Return type: tf.Tensor
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inputs¶ Default inputs of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the inputs of the network.
Returns: Default inputs of the model. Return type: list[tf.Tensor]
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outputs¶ Default outputs of the model.
When the model is built the first time, by default it creates the ‘default’ network. This property creates a reference to the outputs of the network.
Returns: Default outputs of the model. Return type: list[tf.Tensor]
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state_info_specs¶ State info specification.
Returns: - keys and shapes for the information related to the
- module’s state when taking an action.
Return type: List[str]
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state_info_keys¶ State info keys.
Returns: - keys for the information related to the module’s state
- when taking an input.
Return type: List[str]
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network_output_spec(self)¶ Network output spec.
Returns: List of key(str) for the network outputs. Return type: list[str]
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build(self, *inputs, name=None)¶ Build a Network with the given input(s).
* Do not call tf.global_variable_initializers() after building a model as it will reassign random weights to the model. The parameters inside a model will be initialized when calling build(). *
It uses the same, fixed variable scope for all Networks, to ensure parameter sharing. Different Networks must have an unique name.
Parameters: Raises: ValueError– When a Network with the same name is already built.Returns: - Output tensors of the model with the given
inputs.
Return type: list[tf.Tensor]
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network_input_spec(self)¶ Network input spec.
Returns: List of key(str) for the network inputs. Return type: list[str]
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reset(self, do_resets=None)¶ Reset the module.
This is effective only to recurrent modules. do_resets is effective only to vectoried modules.
For a vectorized modules, do_resets is an array of boolean indicating which internal states to be reset. The length of do_resets should be equal to the length of inputs.
Parameters: do_resets (numpy.ndarray) – Bool array indicating which states to be reset.
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terminate(self)¶ Clean up operation.
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get_trainable_vars(self)¶ Get trainable variables.
Returns: - A list of trainable variables in the current
- variable scope.
Return type: List[tf.Variable]
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get_global_vars(self)¶ Get global variables.
Returns: - A list of global variables in the current
- variable scope.
Return type: List[tf.Variable]
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get_regularizable_vars(self)¶ Get all network weight variables in the current scope.
Returns: - A list of network weight variables in the
- current variable scope.
Return type: List[tf.Variable]
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get_params(self)¶ Get the trainable variables.
Returns: - A list of trainable variables in the current
- variable scope.
Return type: List[tf.Variable]
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get_param_shapes(self)¶ Get parameter shapes.
Returns: A list of variable shapes. Return type: List[tuple]
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get_param_values(self)¶ Get param values.
Returns: - Values of the parameters evaluated in
- the current session
Return type: np.ndarray
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set_param_values(self, param_values)¶ Set param values.
Parameters: param_values (np.ndarray) – A numpy array of parameter values.
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flat_to_params(self, flattened_params)¶ Unflatten tensors according to their respective shapes.
Parameters: flattened_params (np.ndarray) – A numpy array of flattened params. Returns: - A list of parameters reshaped to the
- shapes specified.
Return type: List[np.ndarray]